@article{FisserKhorsandiWegmannetal.2022, author = {Fisser, Henrik and Khorsandi, Ehsan and Wegmann, Martin and Baier, Frank}, title = {Detecting moving trucks on roads using Sentinel-2 data}, series = {Remote Sensing}, volume = {14}, journal = {Remote Sensing}, number = {7}, issn = {2072-4292}, doi = {10.3390/rs14071595}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-267174}, year = {2022}, abstract = {In most countries, freight is predominantly transported by road cargo trucks. We present a new satellite remote sensing method for detecting moving trucks on roads using Sentinel-2 data. The method exploits a temporal sensing offset of the Sentinel-2 multispectral instrument, causing spatially and spectrally distorted signatures of moving objects. A random forest classifier was trained (overall accuracy: 84\%) on visual-near-infrared-spectra of 2500 globally labelled targets. Based on the classification, the target objects were extracted using a developed recursive neighbourhood search. The speed and the heading of the objects were approximated. Detections were validated by employing 350 globally labelled target boxes (mean F\(_1\) score: 0.74). The lowest F\(_1\) score was achieved in Kenya (0.36), the highest in Poland (0.88). Furthermore, validated at 26 traffic count stations in Germany on in sum 390 dates, the truck detections correlate spatio-temporally with station figures (Pearson r-value: 0.82, RMSE: 43.7). Absolute counts were underestimated on 81\% of the dates. The detection performance may differ by season and road condition. Hence, the method is only suitable for approximating the relative truck traffic abundance rather than providing accurate absolute counts. However, existing road cargo monitoring methods that rely on traffic count stations or very high resolution remote sensing data have limited global availability. The proposed moving truck detection method could fill this gap, particularly where other information on road cargo traffic are sparse by employing globally and freely available Sentinel-2 data. It is inferior to the accuracy and the temporal detail of station counts, but superior in terms of spatial coverage.}, language = {en} } @article{HalbgewachsWegmanndaPonte2022, author = {Halbgewachs, Magdalena and Wegmann, Martin and da Ponte, Emmanuel}, title = {A spectral mixture analysis and landscape metrics based framework for monitoring spatiotemporal forest cover changes: a case study in Mato Grosso, Brazil}, series = {Remote Sensing}, volume = {14}, journal = {Remote Sensing}, number = {8}, issn = {2072-4292}, doi = {10.3390/rs14081907}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-270644}, year = {2022}, abstract = {An increasing amount of Brazilian rainforest is being lost or degraded for various reasons, both anthropogenic and natural, leading to a loss of biodiversity and further global consequences. Especially in the Brazilian state of Mato Grosso, soy production and large-scale cattle farms led to extensive losses of rainforest in recent years. We used a spectral mixture approach followed by a decision tree classification based on more than 30 years of Landsat data to quantify these losses. Research has shown that current methods for assessing forest degradation are lacking accuracy. Therefore, we generated classifications to determine land cover changes for each year, focusing on both cleared and degraded forest land. The analyses showed a decrease in forest area in Mato Grosso by 28.8\% between 1986 and 2020. In order to measure changed forest structures for the selected period, fragmentation analyses based on diverse landscape metrics were carried out for the municipality of Colniza in Mato Grosso. It was found that forest areas experienced also a high degree of fragmentation over the study period, with an increase of 83.3\% of the number of patches and a decrease of the mean patch area of 86.1\% for the selected time period, resulting in altered habitats for flora and fauna.}, language = {en} } @techreport{MuellerSchererLorenzenAmmeretal.2022, author = {M{\"u}ller, J{\"o}rg and Scherer-Lorenzen, Michael and Ammer, Christian and Eisenhauer, Nico and Seidel, Dominik and Schuldt, Bernhard and Biedermann, Peter and Schmitt, Thomas and K{\"u}nzer, Claudia and Wegmann, Martin and Cesarz, Simone and Peters, Marcell and Feldhaar, Heike and Steffan-Dewenter, Ingolf and Claßen, Alice and B{\"a}ssler, Claus and von Oheimb, Goddert and Fichtner, Andreas and Thorn, Simon and Weisser, Wolfgang}, title = {BETA-FOR: Erh{\"o}hung der strukturellen Diversit{\"a}t zwischen Waldbest{\"a}nden zur Erh{\"o}hung der Multidiversit{\"a}t und Multifunktionalit{\"a}t in Produktionsw{\"a}ldern. Antragstext f{\"u}r die DFG Forschungsgruppe FOR 5375}, doi = {10.25972/OPUS-29084}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-290849}, pages = {210}, year = {2022}, abstract = {Der in j{\"u}ngster Zeit beobachtete kontinuierliche Verlust der β-Diversit{\"a}t in {\"O}kosystemen deutet auf homogene Gemeinschaften auf Landschaftsebene hin, was haupts{\"a}chlich auf die steigende Landnutzungsintensit{\"a}t zur{\"u}ckgef{\"u}hrt wird. Biologische Vielfalt ist mit zahlreichen Funktionen und der Stabilit{\"a}t von {\"O}kosystemen verkn{\"u}pft. Es ist daher zu erwarten, dass eine abnehmende β-Diversit{\"a}t auch die Multifunktionalit{\"a}t verringert. Wir kombinieren hier Fachwissen aus der Forstwissenschaft, der {\"O}kologie, der Fernerkundung, der chemischen {\"O}kologie und der Statistik in einem gemeinschaftlichen und experimentellen β-Diversit{\"a}tsdesign, um einerseits die Auswirkungen der Homogenisierung zu bewerten und andererseits Konzepte zu entwickeln, um negative Auswirkungen durch Homogenisierung in W{\"a}ldern r{\"u}ckg{\"a}ngig zu machen. Konkret werden wir uns mit der Frage besch{\"a}ftigen, ob die Verbesserung der strukturellen β-Komplexit{\"a}t (ESBC) in W{\"a}ldern durch Waldbau oder nat{\"u}rliche St{\"o}rungen die Biodiversit{\"a}t und Multifunktionalit{\"a}t in ehemals homogenen Produktionsw{\"a}ldern erh{\"o}hen kann. Unser Ansatz wird m{\"o}gliche Mechanismen hinter den beobachteten Homogenisierungs-Diversit{\"a}ts-Beziehungen identifizieren und zeigen, wie sich diese auf die Multifunktionalit{\"a}t auswirken. An elf Standorten in ganz Deutschland haben wir dazu zwei Waldbest{\"a}nde als zwei kleine "Waldlandschaften" ausgew{\"a}hlt. In einem dieser beiden Best{\"a}nde haben wir ESBC (Enhancement of Structural Beta Complexity)-Behandlungen durchgef{\"u}hrt. Im zweiten, dem Kontrollbestand, werden wir die gleich Anzahl 50x50m Parzellen ohne ESBC einrichten. Auf allen Parzellen werden wir 18 taxonomische Artengruppen aller trophischer Ebenen und 21 {\"O}kosystemfunktionen, einschließlich der wichtigsten Funktionen in W{\"a}ldern der gem{\"a}ßigten Zonen, messen. Der statistische Rahmen wird eine umfassende Analyse der Biodiversit{\"a}t erm{\"o}glichen, indem verschiedenen Aspekte (taxonomische, funktionelle und phylogenetische Vielfalt) auf verschiedenen Skalenebenen (α-, β-, γ-Diversit{\"a}t) quantifiziert werden. Um die Gesamtdiversit{\"a}t zu kombinieren, werden wir das Konzept der Multidiversit{\"a}t auf die 18 Taxa anwenden. Wir werden neue Ans{\"a}tze zur Quantifizierung und Aufteilung der Multifunktionalit{\"a}t auf α- und β-Skalen verwenden und entwickeln. Durch die experimentelle Beschreibung des Zusammenhangs zwischen β-Diversit{\"a}t und Multifunktionalit{\"a}t in einer Reallandschaft wird unsere Forschung einen neuen Weg einschlagen. Dar{\"u}ber hinaus werden wir dazu beitragen, verbesserte Leitlinien f{\"u}r waldbauliche Konzepte und f{\"u}r das Management nat{\"u}rlicher St{\"o}rungen zu entwickeln, um Homogenisierungseffekte der Vergangenheit umzukehren.}, subject = {Wald{\"o}kosystem}, language = {en} } @article{RemelgadoLeutnerSafietal.2018, author = {Remelgado, Ruben and Leutner, Benjamin and Safi, Kamran and Sonnenschein, Ruth and Kuebert, Carina and Wegmann, Martin}, title = {Linking animal movement and remote sensing - mapping resource suitability from a remote sensing perspective}, series = {Remote Sensing in Ecology and Conservation}, volume = {4}, journal = {Remote Sensing in Ecology and Conservation}, number = {3}, doi = {10.1002/rse2.70}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-225199}, pages = {211-224}, year = {2018}, abstract = {Optical remote sensing is an important tool in the study of animal behavior providing ecologists with the means to understand species-environment interactions in combination with animal movement data. However, differences in spatial and temporal resolution between movement and remote sensing data limit their direct assimilation. In this context, we built a data-driven framework to map resource suitability that addresses these differences as well as the limitations of satellite imagery. It combines seasonal composites of multiyear surface reflectances and optimized presence and absence samples acquired with animal movement data within a cross-validation modeling scheme. Moreover, it responds to dynamic, site-specific environmental conditions making it applicable to contrasting landscapes. We tested this framework using five populations of White Storks (Ciconia ciconia) to model resource suitability related to foraging achieving accuracies from 0.40 to 0.94 for presences and 0.66 to 0.93 for absences. These results were influenced by the temporal composition of the seasonal reflectances indicated by the lower accuracies associated with higher day differences in relation to the target dates. Additionally, population differences in resource selection influenced our results marked by the negative relationship between the model accuracies and the variability of the surface reflectances associated with the presence samples. Our modeling approach spatially splits presences between training and validation. As a result, when these represent different and unique resources, we face a negative bias during validation. Despite these inaccuracies, our framework offers an important basis to analyze species-environment interactions. As it standardizes site-dependent behavioral and environmental characteristics, it can be used in the comparison of intra- and interspecies environmental requirements and improves the analysis of resource selection along migratory paths. Moreover, due to its sensitivity to differences in resource selection, our approach can contribute toward a better understanding of species requirements.}, language = {en} } @article{UlloaTorrealbaStahlmannWegmannetal.2020, author = {Ulloa-Torrealba, Yrneh and Stahlmann, Reinhold and Wegmann, Martin and Koellner, Thomas}, title = {Over 150 years of change: object-oriented analysis of historical land cover in the Main river catchment, Bavaria/Germany}, series = {Remote Sensing}, volume = {12}, journal = {Remote Sensing}, number = {24}, issn = {2072-4292}, doi = {10.3390/rs12244048}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-220029}, year = {2020}, abstract = {The monitoring of land cover and land use change is critical for assessing the provision of ecosystem services. One of the sources for long-term land cover change quantification is through the classification of historical and/or current maps. Little research has been done on historical maps using Object-Based Image Analysis (OBIA). This study applied an object-based classification using eCognition tool for analyzing the land cover based on historical maps in the Main river catchment, Upper Franconia, Germany. This allowed land use change analysis between the 1850s and 2015, a time span which covers the phase of industrialization of landscapes in central Europe. The results show a strong increase in urban area by 2600\%, a severe loss of cropland (-24\%), a moderate reduction in meadows (-4\%), and a small gain in forests (+4\%). The method proved useful for the application on historical maps due to the ability of the software to create semantic objects. The confusion matrix shows an overall accuracy of 82\% for the automatic classification compared to manual reclassification considering all 17 sample tiles. The minimum overall accuracy was 65\% for historical maps of poor quality and the maximum was 91\% for very high-quality ones. Although accuracy is between high and moderate, coarse land cover patterns in the past and trends in land cover change can be analyzed. We conclude that such long-term analysis of land cover is a prerequisite for quantifying long-term changes in ecosystem services.}, language = {en} } @article{RemelgadoSafiWegmann2020, author = {Remelgado, Ruben and Safi, Kamran and Wegmann, Martin}, title = {From ecology to remote sensing: using animals to map land cover}, series = {Remote Sensing in Ecology and Conservation}, volume = {6}, journal = {Remote Sensing in Ecology and Conservation}, number = {1}, doi = {10.1002/rse2.126}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-225200}, pages = {93-104}, year = {2020}, abstract = {Land cover is a key variable in monitoring applications and new processing technologies made deriving this information easier. Yet, classification algorithms remain dependent on samples collected on the field and field campaigns are limited by financial, infrastructural and political boundaries. Here, animal tracking data could be an asset. Looking at the land cover dependencies of animal behaviour, we can obtain land cover samples over places that are difficult to access. Following this premise, we evaluated the potential of animal movement data to map land cover. Specifically, we used 13 White Storks (Cicona cicona) individuals of the same population to map agriculture within three test regions distributed along their migratory track. The White Stork has adapted to foraging over agricultural lands, making it an ideal source of samples to map this land use. We applied a presence-absence modelling approach over a Normalized Difference Vegetation Index (NDVI) time series and validated our classifications, with high-resolution land cover information. Our results suggest White Stork movement is useful to map agriculture, however, we identified some limitations. We achieved high accuracies (F1-scores > 0.8) for two test regions, but observed poor results over one region. This can be explained by differences in land management practices. The animals preferred agriculture in every test region, but our data showed a biased distribution of training samples between irrigated and non-irrigated land. When both options occurred, the animals disregarded non-irrigated land leading to its misclassification as non-agriculture. Additionally, we found difference between the GPS observation dates and the harvest times for non-irrigated crops. Given the White Stork takes advantage of managed land to search for prey, the inactivity of these fields was the likely culprit of their underrepresentation. Including more species attracted to agriculture - with other land-use dependencies and observation times - can contribute to better results in similar applications.}, language = {en} } @article{PhilippWegmannKuebertFlock2021, author = {Philipp, Marius and Wegmann, Martin and K{\"u}bert-Flock, Carina}, title = {Quantifying the Response of German Forests to Drought Events via Satellite Imagery}, series = {Remote Sensing}, volume = {13}, journal = {Remote Sensing}, number = {9}, issn = {2072-4292}, doi = {10.3390/rs13091845}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-239575}, year = {2021}, abstract = {Forest systems provide crucial ecosystem functions to our environment, such as balancing carbon stocks and influencing the local, regional and global climate. A trend towards an increasing frequency of climate change induced extreme weather events, including drought, is hereby a major challenge for forest management. Within this context, the application of remote sensing data provides a powerful means for fast, operational and inexpensive investigations over large spatial scales and time. This study was dedicated to explore the potential of satellite data in combination with harmonic analyses for quantifying the vegetation response to drought events in German forests. The harmonic modelling method was compared with a z-score standardization approach and correlated against both, meteorological and topographical data. Optical satellite imagery from Landsat and the Moderate Resolution Imaging Spectroradiometer (MODIS) was used in combination with three commonly applied vegetation indices. Highest correlation scores based on the harmonic modelling technique were computed for the 6th harmonic degree. MODIS imagery in combination with the Normalized Difference Vegetation Index (NDVI) generated hereby best results for measuring spectral response to drought conditions. Strongest correlation between remote sensing data and meteorological measures were observed for soil moisture and the self-calibrated Palmer Drought Severity Index (scPDSI). Furthermore, forests regions over sandy soils with pine as the dominant tree type were identified to be particularly vulnerable to drought. In addition, topographical analyses suggested mitigated drought affects along hill slopes. While the proposed approaches provide valuable information about vegetation dynamics as a response to meteorological weather conditions, standardized in-situ measurements over larger spatial scales and related to drought quantification are required for further in-depth quality assessment of the used methods and data.}, language = {en} } @article{Schwalb‐WillmannRemelgadoSafietal.2020, author = {Schwalb-Willmann, Jakob and Remelgado, Ruben and Safi, Kamran and Wegmann, Martin}, title = {moveVis: Animating movement trajectories in synchronicity with static or temporally dynamic environmental data in R}, series = {Methods in Ecology and Evolution}, volume = {11}, journal = {Methods in Ecology and Evolution}, number = {5}, doi = {10.1111/2041-210X.13374}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-214856}, pages = {664 -- 669}, year = {2020}, abstract = {Visualizing movement data is challenging: While traditional spatial data can be sufficiently displayed as two-dimensional plots or maps, movement trajectories require the representation of time in a third dimension. To address this, we present moveVis, an R package, which provides tools to animate movement trajectories, overlaying simultaneous uni- or multi-temporal raster imagery or vector data. moveVis automates the processing of movement and environmental data to turn such into an animation. This includes (a) the regularization of movement trajectories enforcing uniform time instances and intervals across all trajectories, (b) the frame-wise mapping of movement trajectories onto temporally static or dynamic environmental layers, (c) the addition of customizations, for example, map elements or colour scales and (d) the rendering of frames into an animation encoded as GIF or video file. moveVis is designed to display interactions and concurrencies of animal movement and environmental data. We present examples and use cases, ranging from data exploration to visualizing scientific findings. Static spatial plots of movement data disregard the temporal dimension that distinguishes movement from other spatial data. In contrast, animations allow to display relocation in both time and space. We deem animations a powerful way to visually explore movement data, frame analytical findings and display potential interactions with spatially continuous and temporally dynamic environmental covariates.}, language = {en} } @article{LauschBorgBumbergeretal.2018, author = {Lausch, Angela and Borg, Erik and Bumberger, Jan and Dietrich, Peter and Heurich, Marco and Huth, Andreas and Jung, Andr{\´a}s and Klenke, Reinhard and Knapp, Sonja and Mollenhauer, Hannes and Paasche, Hendrik and Paulheim, Heiko and Pause, Marion and Schweitzer, Christian and Schmulius, Christiane and Settele, Josef and Skidmore, Andrew K. and Wegmann, Martin and Zacharias, Steffen and Kirsten, Toralf and Schaepman, Michael E.}, title = {Understanding forest health with remote sensing, part III: requirements for a scalable multi-source forest health monitoring network based on data science approaches}, series = {Remote Sensing}, volume = {10}, journal = {Remote Sensing}, number = {7}, issn = {2072-4292}, doi = {10.3390/rs10071120}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-197691}, pages = {1120}, year = {2018}, abstract = {Forest ecosystems fulfill a whole host of ecosystem functions that are essential for life on our planet. However, an unprecedented level of anthropogenic influences is reducing the resilience and stability of our forest ecosystems as well as their ecosystem functions. The relationships between drivers, stress, and ecosystem functions in forest ecosystems are complex, multi-faceted, and often non-linear, and yet forest managers, decision makers, and politicians need to be able to make rapid decisions that are data-driven and based on short and long-term monitoring information, complex modeling, and analysis approaches. A huge number of long-standing and standardized forest health inventory approaches already exist, and are increasingly integrating remote-sensing based monitoring approaches. Unfortunately, these approaches in monitoring, data storage, analysis, prognosis, and assessment still do not satisfy the future requirements of information and digital knowledge processing of the 21st century. Therefore, this paper discusses and presents in detail five sets of requirements, including their relevance, necessity, and the possible solutions that would be necessary for establishing a feasible multi-source forest health monitoring network for the 21st century. Namely, these requirements are: (1) understanding the effects of multiple stressors on forest health; (2) using remote sensing (RS) approaches to monitor forest health; (3) coupling different monitoring approaches; (4) using data science as a bridge between complex and multidimensional big forest health (FH) data; and (5) a future multi-source forest health monitoring network. It became apparent that no existing monitoring approach, technique, model, or platform is sufficient on its own to monitor, model, forecast, or assess forest health and its resilience. In order to advance the development of a multi-source forest health monitoring network, we argue that in order to gain a better understanding of forest health in our complex world, it would be conducive to implement the concepts of data science with the components: (i) digitalization; (ii) standardization with metadata management after the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles; (iii) Semantic Web; (iv) proof, trust, and uncertainties; (v) tools for data science analysis; and (vi) easy tools for scientists, data managers, and stakeholders for decision-making support.}, language = {en} } @article{vanToorNewmanTakekawaetal.2016, author = {van Toor, Mari{\"e}lle L. and Newman, Scott H. and Takekawa, John Y. and Wegmann, Martin and Safi, Kamran}, title = {Temporal segmentation of animal trajectories informed by habitat use}, series = {Ecosphere}, volume = {7}, journal = {Ecosphere}, number = {10}, doi = {10.1002/ecs2.1498}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-164970}, pages = {e01498}, year = {2016}, abstract = {Most animals live in seasonal environments and experience very different conditions throughout the year. Behavioral strategies like migration, hibernation, and a life cycle adapted to the local seasonality help to cope with fluctuations in environmental conditions. Thus, how an individual utilizes the environment depends both on the current availability of habitat and the behavioral prerequisites of the individual at that time. While the increasing availability and richness of animal movement data has facilitated the development of algorithms that classify behavior by movement geometry, changes in the environmental correlates of animal movement have so far not been exploited for a behavioral annotation. Here, we suggest a method that uses these changes in individual-environment associations to divide animal location data into segments of higher ecological coherence, which we term niche segmentation. We use time series of random forest models to evaluate the transferability of habitat use over time to cluster observational data accordingly. We show that our method is able to identify relevant changes in habitat use corresponding to both changes in the availability of habitat and how it was used using simulated data, and apply our method to a tracking data set of common teal (Anas crecca). The niche segmentation proved to be robust, and segmented habitat suitability outperformed models neglecting the temporal dynamics of habitat use. Overall, we show that it is possible to classify animal trajectories based on changes of habitat use similar to geometric segmentation algorithms. We conclude that such an environmentally informed classification of animal trajectories can provide new insights into an individuals' behavior and enables us to make sensible predictions of how suitable areas might be connected by movement in space and time.}, language = {en} }